Current Research Projects

Research in my laboratory seeks to elucidate the computational principles
and neural mechanisms underlying visual perception. We use computational,
mathematical and neurophysiological experimental techniques to address the
following fundamental issues in the fields of computational and biological
vision.

Statistcal and ecological approaches to higher order neural codes.

The study of neural representation should start with a rigorous study of
the statistical regularities in the visual environment. I have adopted the
Gibsonian ecological approach in our quest to understand how the brain
represents and computes 2D curves and 3D surfaces. We developed databases
of 2D and 3D natural scenes and applied statistical and machine learning
techniques to discover the statistical structures in the data. This has
allowed us to predict plausible neural representations based on the
principle of efficient coding. We are currently carrying out
neurophysiological experiments to evaluate the various possible candidates
for the neural representations of mid-level vision (2D shapes and 3D
surfaces). We have developed methods to discover how neurons encode
information and to decode visual stimuli based on neural responses. We are
experimenting with the implantation of microelectrode arrays that can
record from hundreds of neurons simultaneously. Our long term goal is to
decode and reconstruct computationally the mental images that are
represented in our visual cortex.

Learning, adaptation and development in neural systems.

Learning and adaptation are what make biological systems so much more
robust and powerful than current man-made vision systems. My current
research explores the principles underlying adaptation and learning in the
visual system at different time scales and at the level of neurons and of
neural systems. We have studied theoretically how neurons adapt
dynamically to the statistical context of the visual stimuli. We have
determined biophysical features in spiking neurons that make them adapt,
and have isolated the statistical features in natural stimuli that drive
neuronal adaptation. In our neurophysiological studies, we have
demonstrated that the neural machinery of perceptual processing is very
flexible and subject to modification by behavioral experience. We are
currently undertaking computational and neurophysiological studies on the
time course and dynamical processes of neural plasticity and visual
development. We hope these studies will provide insights to the design
principles underlying the adaptation of adult visual systems and the
development of infant vision.

Principles and algorithms of hierarchical perceptual inference.

Perceptual inference is an active and creative process that constructs an
internal interpretation of the outside world in our mind. Bottom-up
information from the retina is only a clue that starts the inference
process, which is affected by various global scene contextual factors and
perceptual experience. We have carried out a series of neurophysiological
experiments to demonstrate the influence of a variety of contextual
factors in shaping visual processing in the early visual areas. We have
demonstrated experimentally that visual processing likely involves the
entire hierarchical circuit interactively. We are developing a
computational framework for perceptual inference based on hierarchical
Bayesian inference to elucidate the rules of recurrent feedback in
cortical circuits, and to understand the computational algorithms
underlying the inference of shapes and surfaces of visual objects in our
mind.

Our research is currently supported by two grants from the National
Science Foundation, a center grant from NIMH and a center grant from NIH,
two NSF graduate student fellowships and a NRSA postdoctoral fellowsip
from NEI. For further details of research in my laboratory, please visit
the Active Perception
Laboratory web page.